In recent years, due to larger GPU memory and faster compute capabilities, three-dimensional grid-like representations have been explored. The importance of three-dimensional structural information can be highlighted as large-scale datasets such as the
open graph benchmark - large-scale challenge (OGB-LSC) [1]} updated their datasets to include 3D coordinate information. This follows the trend as [2]} employed 3D conformer models to extend PubChem [3]}, an open repository for small molecules designed to study biological activity. 3D grids can encode both geometric and chemical information, while also being generalizable to represent many different classes of materials. Including geometric and chemical information to the input of deep learning models provides inductive bias to improve learning results  [4]}, [5]}. Representations that can be shared across many different classes enable the use of transfer learning [6]}. Significantly, this enables the use of deep learning, traditionally requiring large datasets, to be used on classes of materials with few samples by pre-training on larger datasets.
